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Pixie Trade Promotion Management and Optimization Platform

According to recent research, consumer packaged goods companies spend more than $200 billion trade promotions like discounts, displays, and special offers, which are often their second-largest line item expense after costs of goods sold and absorb more than 25% of sales revenue.

Even so, other findings suggest that as much as 59% of global promotions – and a full 82% in the United States – do not actually drive profits.

Designed to help ensure that yours do, Pixie from Lingaro is a comprehensive and easy-to-use trade promotion management and optimization platform offering clear insights into historical trade spend and success ratio predictions of your future promotion profitability.

Strategize around data-driven
predictions

Tailor promotions to market conditions and customer needs with input from internal and external stakeholders. Receive automated recommendations of the most valuable, promo- tion-sensitive SKUs based on portfolio analyses accounting for intra-portfolio cannibalization ratios and competitors’ campaigns.

Plan for success

Realize the full profit potential of your promotions by defining clear goals for each one, adhering to long-term guidelines, and enabling short-term elasticity. Ease inter-departmental coordination and assign clear responsibilities to internal teams. Share data amongst business partners to improve forecast accuracy and ensure process consistency with a clean, sophisticated interface that integrates easily with any reporting system.

Execute smoothly

Define easy-to-understand rules for promotions at the single-store level. Ease coordination and assign clear design, approval, and launch responsibilities across Sales, Marketing, Finance, and external teams. Deploy trade funds to boost net revenues, improve incremental sales, and maximize ROI, which is fully measurable against sales forecasts and baseline calculations.

Evaluate areas for improvement

Standardize reporting and analyze easily-understandable KPIs at all levels, from basic to advanced, covering cannibalization, competitive reactions, and planned vs. actual costs, revenue, and incremental sales. Store historical data on previous promotions in one repository to compare efficiency and accuracy and build an easy, clear learning process for future optimizations and predictions.

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